Enhancing Supply Chain Using Machine Learning and Data Analytics: Case Study for Shippment Latency Prediction

Abstract

Artificial Intelligence (AI) is revolutionizing supply chain management (SCM) by enabling predictive analytics, optimizing logistics, and identifying inefficiencies. This study explores the application of machine learning in predicting shipment latency, a critical factor in supply chain optimization. By leveraging AI-driven models, we aim to enhance decision-making, reduce operational costs, and improve logistical planning. The research evaluates the effectiveness of predictive analytics in mitigating delays and streamlining transportation routes, contributing to increased efficiency and resilience in SCM. Findings highlight AI's potential in enhancing supply chain agility, improving responsiveness to demand fluctuations, and optimizing resource allocation. This study adds to the growing body of literature on AI applications in SCM by demonstrating the value of predictive modeling in improving supply chain performance.